HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study.

Liu, Zeyu; Khojandi, Anahita; Mohammed, Akram; Li, Xueping; Chinthala, Lokesh K; Davis, Robert L; Kamaleswaran, Rishikesan

Liu, Zeyu; Khojandi, Anahita; Mohammed, Akram; Li, Xueping; Chinthala, Lokesh K; Davis, Robert L; Kamaleswaran, Rishikesan.

Afiliación

Liu Z; Department of Industrial & Systems Engineering, The University of Tennessee, Knoxville, TN, 37996, USA.
Khojandi A; Department of Industrial & Systems Engineering, The University of Tennessee, Knoxville, TN, 37996, USA. Electronic address: khojandi@utk.edu.
Mohammed A; Center for Biomedical Informatics-Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, 38103, USA.
Li X; Department of Industrial & Systems Engineering, The University of Tennessee, Knoxville, TN, 37996, USA.
Chinthala LK; Center for Biomedical Informatics-Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, 38103, USA.
Davis RL; Center for Biomedical Informatics-Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, 38103, USA.
Kamaleswaran R; Departments of Biomedical Informatics, Pediatrics, and Emergency Medicine, Emory University School of Medicine, Atlanta, GA, 30322, USA.

Comput Biol Med ; 131: 104255, 2021 04.

Article en En | MEDLINE | ID: mdl-33639353

Asunto(s)

Aprendizaje Automático; Sepsis; Diagnóstico Precoz; Humanos; Valor Predictivo de las Pruebas; Sepsis/diagnóstico; Sepsis/epidemiología

Palabras clave

Alarm management; Early sepsis detection; False alarms; Real-time machine learning; Statistical analysis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sepsis / Aprendizaje Automático Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Revista: Comput Biol Med Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

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